Proposed Control Approach Research Articles

Adaptive Multi-Switching-Based Global Tracking Control for Switched Nonlinear Systems With Prescribed Performance

This paper is concerned with the tracking control for a class of switched nonlinear systems subject to prescribed performance. Firstly, a barrier function and a normalized function are introduced to achieve prescribed performance control, which ensures that the tracking error evolves within prescribed boundary. Then, multi-dimensional Taylor network provides a new approach for how to estimate the nonlinearity arising from the backstepping control process. Significantly, the proposed multi-switching-based adaptive controller realizes that all signals in the closed-loop system are globally uniformly ultimately bounded while ensuring asymptotic tracking. Different from most existing network-approximation-based control strategies, the developed method in this paper is not only independent of the initial state, but also can achieve global stability. Finally, it is easy to verify the effectiveness of the proposed control method through two simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research is motivated by the fact that the control ideas of many practical engineering systems can be provided by making a profound study on switched nonlinear systems. However, most of the existing results focused on semi-global stability of systems. Therefore, this study devotes to develop a novel adaptive prescribed performance control strategy, which can not only ensure global stability of closed-loop system but also realize the asymptotic tracking of the switched nonlinear systems. It is worth noting that the proposed control approach has great significance for many practical systems, such as circuit systems and single-link inverted pendulum systems.

Nonlinear Observer-Based Visual Servoing and Vibration Control of Flexible Robotic Manipulators With a Fixed Camera.

This article studies the visual servoing and vibration suppression control for flexible manipulators when the system states are unmeasurable and only the image feedback is available. The dynamic equations of flexible manipulators are decomposed into the slow and fast subsystems based on the singular perturbation theory. The nonlinear observers based on the state transformation using the Lie derivatives are proposed to estimate the unmeasurable system states and unknown camera intrinsic parameters at the same time. Then, the image-based controllers utilizing the estimated states are, respectively, designed in the slow and fast subsystems to regulate the image positions of feature points and suppress the vibration of flexible manipulators simultaneously. In the proposed approach, only the visual feedback is required to generate the control input for flexible manipulators, which simplifies the controller implementation. The stability of the proposed control scheme is proved based on the Lyapunov theory. Finally, experimental results on a flexible single-link manipulator are provided to demonstrate the effectiveness of the proposed control approach.

Finite-time disturbance observer-based levitation control for vehicle-guideway coupling systems

In this study, a novel composite control scheme for the vehicle-guideway coupling systems is proposed, consisting of FTDOs and a FTC, aiming to address the challenges of unknown disturbances and vibration suppression. Specifically, this method adopts a single magnet-track coupling model and introduces a finite-time disturbance observer (FTDO) that utilizes only measured electromagnet-side signals to estimate unmeasurable states and unknown disturbances. Based on the estimated information provided by the FTDO, a finite-time control (FTC) scheme is developed, which simultaneously handles the problems of disturbance compensation and finite-time tracking control. Additionally, the finite-time stability of the levitation system is analyzed and proven. Finally, simulation and experimental results are given to demonstrate the feasibility and superiority of the proposed control approach.

Wind-Driven Position Sensorless Switched Reluctance Generator and Diesel Generator Based Microgrid for Optimal Fuel Consumption and Power Blackout Mitigation

This paper proposes a battery-supported wind power generation system (WPGS) with a mechanical position sensorless switched reluctance generator (SRG) and a diesel generator set (DGS) to enable uninterrupted electricity to remote locations. A sliding mode position observer (SMPO) estimates the SRG speed and position based on the generator voltages and currents. The proposed control approach enhances the consideration of dynamic variations in demand and generation in light of the potential for off-grid operation of small-scale WPGS in regions with a weak or nonexistent grid. Diesel engines perform best at 80%-100% of full capacity. At this mechanical loading, CO and other greenhouse gases are the lowest. The proposed approach keeps the DGS at 80%-100% loading regardless of mechanical load to maximize efficiency. In such a microgrid, the power quality (PQ) concern is dominant due to the use of local nonlinear loads. Hence, a joint logarithmic hyperbolic cosine adaptive filter (JLHCAF) is designed for the microgrid to mitigate the PQ issues emerging due to the nonlinear loads.

Adaptive Active Disturbance Rejection Control for Vehicle Steer-by-Wire under Communication Time Delays

In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance estimation performances. Based on the accurate estimations of the adaptive extended state observer, the proposed adaptive full state error feedback controller is equipped with variable gains driven by the tracking error to develop control precision. The integration of the advantages of the adaptive extended state observer and the adaptive full state error feedback controller can improve the dynamic transient and static steady-state effectiveness, respectively. To assess the superior performance of the proposed adaptive active disturbance rejection control, a comparative analysis is conducted between the proposed control scheme and the classical active disturbance rejection control in two different cases. It is worth noting that the active disturbance rejection control serves as a benchmark for evaluating the performance of the proposed control approach. The results from the comparison studies executing two simulated cases validate the superiority of the suggested control, in which estimation, tracking response rate, and steering angle precision are greatly improved by the scheme proposed in this article.

Real-Time HIL Simulation of Nonlinear Generalized Model Predictive-Based High-Order SMC for Permanent Magnet Synchronous Machine Drive

The dynamics of the permanent magnet synchronous motor (PMSM) are described by nonlinear equations, which present challenges. Variations in external factors such as unidentified disturbances (loads) and evolving motor properties add complexity to control efforts. To tackle these intricacies and limitations, a nonlinear control approach is essential. Recent attention has turned to employing predictive control techniques for nonlinear multivariable systems, offering an intriguing avenue for research. In this context, this study introduces a novel hybrid control approach that addresses nonlinearity, parametric fluctuations, and external disturbances. The method combines two essential components: first, the outer loop utilizes high-order sliding mode control (HSMC) to optimize torque and trajectory speed, mitigating chattering phenomena while preserving the PMSM’s convergence and robustness traits. The inner loop, known as the current control, employs the newly developed nonlinear robust generalized predictive control (RNGPC) technique. Importantly, this strategy circumvents the need for direct measurement and observation of external disturbances and parameter uncertainties. The proposed strategy follows a two-phase process. Initially, the reference quadratic current is designed using the electromagnetic torque computed via HSMC, subsequently determining the necessary current to achieve the desired torque. The second phase involves computing the controller law through the robust generalized nonlinear predictive control technique. The approach’s strength lies in its ability to maintain stability and convergence in the face of external disturbances and parameter fluctuations, without necessitating precise measurements or knowledge of the disturbances. To validate the proposed control approach, simulation and experimental tests have been conducted across various operational scenarios. The obtained results demonstrate the method’s robustness against external disturbances and parameter changes while ensuring rapid convergence and reliable performance.

Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

ABSTRACT By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO).

HT-PLL-Based Seamless Transition Strategy for DFIG With Power Quality Enhancement Through Fractional-Order Adaptive Filter

The seamless operational mode transition of doubly-fed induction generator (DFIG) driven by wind turbine (WT) is focused here. Moreover, remote geographical allocation of wind farm installations demand power quality (PQ) improvement feature, where the WT becomes a significant contributor to the grid stability. This paper presents a real-time implementation of seamless transition of DFIG based WT with the grid. The seamless transition process is aided by accurate phase angle estimates from a half-tangent (HT) phase locked loop (PLL) technique, which is an improved version of synchronous-reference-frame (SRF) based PLL. The phase detector of SRF-PLL is aided with HT function, which improves the accuracy at steady-state and has faster dynamics under large frequency excursions. For PQ enhancement, an adaptive filter based current control approach is suggested for grid-side converter during grid-feeding operation. The adaptive filter is based on fractional-order gradient-descent total least mean p-norm algorithm. The benefit of this algorithm compared to its parent total least square methods, is its ability to repress impulse noises in the eccentricities of the signal, showing better convergence rate. The proposed control approaches are tested and compared with popular methods in simulation, and an experimental setup validates the satisfactory operation by subjecting through dynamic conditions and load unbalances.

FTDO-based adaptive fuzzy fixed-time tracking control for uncertain unmanned helicopter with output constraints

During the actual flight of unmanned helicopters, there are widespread output constraints, system uncertainties and external disturbances, which bring significant threats to their safe flight. To keep the output constraints from violating predefined boundaries, the barrier Lyapunov function (BLF) is utilized in this paper. The system uncertainties are estimated by the fuzzy logic system (FLS). The compound disturbances composed of the external disturbances and the approximation errors caused by FLS are approximated by the fixed-time disturbance observers (FTDOs). According to the analysis of FTDOs and fixed-time stability theory, the observer errors and state errors converge to a region of the origin in fixed-time, and the appropriate parameters of FTDOs and controller are designed to ensure that the disturbance convergence time is faster than the one of the proposed controller, which further improves the stability of the system. Moreover, the convergence time of the FTDOs FTDOs and the proposed approach only depends on the design parameters under any initial conditions with constrained outputs. The performance of the proposed control approach is verified through the simulation results.

Distributed global output-feedback formation control without velocity measurement for multiple unmanned surface vehicles

This article studies the distributed formation control problem for multiple unmanned surface vehicles (USVs) considering uncertain coefficient matrixes, unmeasurable velocities, and time-varying disturbances. The main contributions are as follows: First, a global coordinate translation is proposed to partially linearize the nonlinear dynamic model equipped with the unmeasurable velocity. Second, based on the global coordinate translation, a novel type of fixed-time extended two-state observer (FTETSO) is developed to estimate unmeasurable velocities and total disturbances for each vehicle. Wherein, the estimation errors will converge to zero within a fixed time. Meanwhile, considering estimation accuracy, a two-state extension is proposed to replace a single-state extension. Third, using a sliding model-based control technique, an FTETSO-based distributed global output-feedback fixed-time formation controller (GOFFC) is elaborately developed. Based on the proposed controller, the fixed-time convergence of the closed-loop system is ensured. Finally, the validity and stability of the proposed control approach are verified by simulations.

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

Robust control for improving performance of grid-synchronized solar PV-BES-wind based microgrid

An integration of hybrid renewable energy systems (HRESs) to the grid results in power quality issues at the point of interconnection. This paper proposes an approach to investigate and mitigate power quality issues and uncertainties associated with integration of a HRES that includes a solar photovoltaic (PV) array, a wind generator, and battery energy storage (BES). The system functions in grid tied mode, standalone mode and seamless mode transition between these modes. Primary objective of proposed control approach is to ensure stable and reliable operation while mitigating effects of power quality issues. To achieve this objective, proposed control approach employs advanced control strategies, including generalized fourth order filter (GFOF) for grid tied mode and phase angle estimation. An off-grid mode uncertainty and disturbances are overcome by proportional resonant based control. GFOF addresses multiple power quality issues associated with grid-connected mode, including robust performance during voltage unbalance, harmonics suppression, DC offset removal, weak grid conditions, and evaluation of unit templates. GFOF extracts fundamental load currents and voltages for evaluation of reference grid currents and phase angle. This precisely estimates frequency and phase angle for seamless synchronization. It provides adaptive mode transition. Power quality of currents is enhanced in both islanded and grid-connected modes through utilization of GFOF control, ensuring that total harmonic distortions (THDs) of grid currents remain below 5% (simulated THD is 1.77% and experimental THD is 3.80%), as specified by the IEEE-1547and 519 standards. A comparative analysis has justified efficacy of control. Simulated and experimental results confirm effectiveness of proposed controllers in various scenarios, demonstrating their ability to provide a favourable solution for controlling unfavourable grid conditions with technical strength and precision.

A Low-Inertia and High-Stiffness Cable-Driven Biped Robot: Design, Modeling, and Control

In this paper, a biped robot system for dynamic walking is presented. It has two 2-degree-of-freedom (DOF) lightweight legs and a 6-DOF hip. All the joint pulleys of the legs are driven by motors that are placed at the hip using steel cables. Since all the heavy motors are mounted at the hip, the biped robot has remarkably low-mass legs beyond the hip, which guarantees low inertia during walking at high speeds. Utilizing cable-amplification mechanisms, high stiffness and strength are achieved, resulting in better control performance compared to conventional direct-driven methods. Techniques are developed to estimate joint-angle errors caused by the elastic deformation of the cables. To achieve smooth control, we introduce the concept of a virtual leg, which is an imaginary leg connecting the hip joint and the ankle joint. A robust control approach based on the “virtual leg” is presented, which considers the variances of the virtual leg length during walking. Experiments are conducted to validate the effectiveness of the mechanical design and the proposed control approach.

Sampled-Data Cooperative Output Regulation: An Adaptive Distributed Continuous-Discrete Observer Approach

This paper studies the sampled-data cooperative output regulation problem for linear multi-agent systems. Firstly, a novel adaptive distributed continuous-discrete observer is established to recover the leader's state, which, on one hand, only relies on the sampling output of the leader, and on the other hand, does not need to store the sampled message from neighbors. Secondly, by solely making use of the digital states of both the agent and the distributed observer, a certainty equivalence control law is synthesized featuring a time-varying feedforward gain. It is rigorously proven that, with this time-varying feedforward gain, the proposed control approach can achieve exponential convergence of the tracking errors given arbitrary time-varying leader's signal, and the upper bound for the sampling intervals is explicitly given. Thirdly, for the class of chain-integrator multi-agent systems, the proposed control approach does not need any restriction on the upper bound of the sampling intervals, and thus would be more practical in certain application scenarios from the perspective of energy saving. The performance of the proposed control approach is validated by the simulation results of inverter-based distributed generation systems.

Multiple Working Mode Control of Robotic Operations on Mechanisms With Unmodelled Constraints With Online Mode Switching

Robotic operations such as opening doors in an unstructured environment pose a challenge for robot control due to unmodelled constraints. In this article, a multiple working mode approach is proposed to control a mobile serial robot manipulator to perform operations on mechanisms with unmodelled constraints. The multiple working mode control switches the working mode of a number of active joints during the operation to relax the problem of over-actuation of the constrained robot manipulator when modeling errors cause excessive internal and external reaction forces. Three practical criteria are derived for the assignment of active and passive control modes; two based on the coordinate partitioning method for multibody dynamics, and another based on minimizing task friction forces. The proposed control method has been implemented on a mobile reconfigurable robot, performing constrained motion along an elliptical trajectory, similar to a door-opening task. Simulation and experimental results are presented to show the effectiveness of the proposed control approach.

A Novel Hybrid Optimization-based Backstepping Fractional Order Sliding Mode Proportional-integral-derivative Controller for Nonlinear Biological System

Nowadays, the role of the control system in the bioengineering field plays a major part due to its significant improvement and demand. Usually, if there are any changes in the parameter of a biological system, then it tends to cause crucial diseases in humans. Previously, various control algorithms have been developed; however, the proper control process failed due to the high error rate, slow convergence, Computational Complexity, Tuning Complexity and inaccurate function. The proposed control strategy should ensure accurate tracking of the desired trajectory or reference signal, even in the presence of uncertainties and disturbances. The control system should be capable of adapting to changes in the system dynamics and maintaining stable operation. This complexity can lead to high computational requirements, making the controller computationally expensive and potentially unsuitable for real-time applications or systems with limited computing resources. To overcome these issues, in this work, the novel Hybrid Ant Lion Moth Flame Optimization (HALMFO) based Backstepping Fractional-Order Sliding mode Proportional-Integral-Derivative (BFO-SPID) controller is proposed for different nonlinear biological systems. The proposed method enhances the efficiency, improved performance, robustness, convergence speed, and solution quality of the optimization process. This paper addresses the challenge of controlling nonlinear biological systems by proposing a novel hybrid optimization-based backstepping approach combined with a fractional order sliding mode proportional-integral-derivative (PID) controller. This work’s essential contribution lies in addressing existing control strategies’ limitations by integrating multiple control techniques to achieve robust tracking performance and disturbance rejection. This system proposes predictive control with state estimation based on the backstepping sliding mode method. The gain parameters of the developed controller are optimized using the HALMFO method. The proposed control approach performance has been tested under biological systems such as Protein, Pancreas, and Genetic Regulatory Network (GRN). The execution of this proposed model is done in MATLAB/Simulink. Furthermore, the performance of the proposed control technique is compared with various state-of-the-art methods in terms of accuracy, Mean Average Error (MAE), Mean Square Error (MSE), settling time, overshoot, and rise time. The comparison illustrates that the projected controller has reached a steady condition with the least error rate in the control application. Thus the developed controller regulates the parameter variations in the biological system as per the desired level, providing significant effective outcomes to complex problems.

Active Disturbance Rejection Control for Distributed Energy Resources in Microgrids

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

Power allocation scheme for grid interactive microgrid with hybrid energy storage system using model predictive control

Grid-interactive microgrids have become a promising alternative to traditional centralized power systems as a result of the rising demand for reliable and sustainable energy solutions. DC microgrids (MG) based upon renewable energy sources (RES) are on the rise due to high energy efficiency and compact size than the AC microgrids. Microgrid equipped with hybrid energy storage system is sensible for a reliable and stable power supply to the system. Implementation of a potent control system makes sure of the system stability. A model predictive control (MPC) based control strategy is proposed due to its easy implementation approach and inclusion nonlinear dynamics & constraints of the controlled system. An efficient power allocation scheme is developed in this paper for a grid-interactive photovoltaic microgrid featuring a hybrid energy storage system (HESS). The system-level power allocation scheme (PAS) considers the real-time data of load demands, generation, market energy cost, and energy storage state-of-charge to actively manage the power flow within the microgrid and also with the utility grid. The proposed control scheme uses a sophisticated model predictive current control for the DC/DC bi-directional converters control and model predictive combined power and voltage control is used for bidirectional interlink converter. The proposed control approach provides faster DC bus voltage recovery. The HESS consisting of battery and supercapacitor is used as energy buffers for responding to the fluctuation of power generation and demand for smoothing the PV power. The potency of the proposed controller is verified using MATLAB/Simulink (2021b) environment. Also, the real-time implementation of the proposed approach is carried out using the OPAL-RT OP4510 simulator.

Power reserve control for utility-scale PV power plants under cloud conditions

The photovoltaic generation growth has posed challenges for the control and operation of contemporary power systems, degrading the system frequency response and stability margins. The decrease of the system equivalent inertia and the performance deterioration of the primary frequency control are the main causes of these problems. Such challenges can be overcome by the provision of ancillary services from photovoltaic power plants. However, the provision of ancillary services based on active power, such as inertial response, frequency regulation, and curtailment, requires the de-loaded operation of the photovoltaic power plants. In such context, this work proposes a de-loaded control approach (or, equivalently, power reserve control) for large photovoltaic power plants under shading conditions caused by cloud movements. This kind of shading condition poses a significant challenge for the de-loaded control, as it causes large and fast variations in the maximum power point of the multiple photovoltaic generation units that compose the photovoltaic power plant. A test system corresponding to a 100 MWac photovoltaic power plant with 25 photovoltaic generation units of 4 MWac is employed to evaluate and validate the proposed control approach. The stochastic distribution and movement of clouds, as well as the solar irradiance over the photovoltaic power plant are generated by an approach based on the fractal geometry. The proposed control was effective and presented a good performance under different cloud coverage levels.

Performance recovery-based fuzzy robust control of networked nonlinear systems against actuator fault: A deferred actuator-switching method

This paper concentrates on the performance recovery-based fuzzy robust control of networked nonlinear systems via a deferred actuator-switching approach. The main design procedure of this paper is divided into three parts: 1) nominal controller design; 2) monitoring function and shifting function design; 3) reconfigurable controller design. With the aid of low-computation method and fuzzy logic systems, a structurally and computationally efficient nominal controller is designed for fault-free. Then, an actuator-switching rule is established based on monitoring the stabilization error. Furthermore, a new error transformation is constructed via designing the shifting function, by which tangent barrier Lyapunov functions are effective at the replacement instant. Finally, the stabilization error recovers into the prescribed performance within a specified period of time, and a reconfigurable controller is designed for the backup actuator. By employing the event-triggering mechanism with the relative threshold strategy, communication loads from controllers to actuators are reduced. Simulation examples are given to confirm the effectiveness of the proposed control approach.